Volume4 ,September 2017.

Volume4,September 2017,

Abstract:Data mining is the analysis step of the “Knowledge discovery in data bases process” [1]. Actually, it is very hard to mine
item-sets which are frequently used in the transactions. To identify frequently used item-sets, parallel algorithms which are used
for mining were developed. These parallel algorithms were developed to balance the data and to maintain equal partitions of data,
among a group of nodes which are to be computed. Because of redundant transactions, there is a significant performance problem
of parallel frequent item-sets mining. Therefore, a data partitioning technique has been developed. FiDoop-DP is a kind of data
partitioning method which is used to divide the data based on item-sets of the transaction which are brought by the clients or
customers. To know better about frequent item-sets i.e., products which are regularly sold together, an algorithm is used for time
consumption while running data which is extremely large. This algorithm is named as Equivalence Class clustering and Lattice
Traversal algorithm (ECLAT). This ECLAT algorithm is combined with the Map-Reduce functionality, and then it gives better
solutions within small amount of time. At the same time ECLAT is combined with Local sensitive hashing technique for better
performance of items which are present at locally present in the data nodes. By combining those two techniques, the performance
of FiDoop increases. This is known by the time taken to mine frequent item-sets. The main goal of this paper is to mine the itemsets
which are prominently used or sold in the market by that it can increase the sales of those products..